knitr::opts_chunk$set(echo = FALSE)
Notes: Most studies fall in single ES type bin, some 2 or 3 ES types. Max 11 ES bins in single study (see below)
Looking at number of studies (bottom row) with number of service types considered (top row)
## .
## 1 2 3 4 5 6 7 8 9 11
## 129 44 26 12 8 2 1 3 1 1
Can be observed in above plot
Probably a word cloud or another sankey (sankey would require binning similar response var’s which is probably too much to bother with)
Notes: Not sure exactly how this will be used, or if the quality is usable?
Notes: pest pathogens more ‘across species’, climate regulation more NA thus less often ESPs considered, Pollination more ‘multiple ESP species’
A ‘Yes’ means that the box for community structure in the Kremen themes question was checked.
Notes: 79 studies look at comm structure influencing function and got included
will have to ask Caitlin about this one (or she might be doing it already?)
Notes: Pest pathogens, Habitat creation, Climate regulation pretty even (which means more Yes than expected), Pollination may have more No’s than expected
SANKEY
SANKEY
not sure exactly what this means or if/how we could test it?
This section contains three subsections. First, there are some basic plots of the overall trends of spatio-temporal scale across all papers. Second, there is a section looking at space-time tradeoffs. Third, there is a section on scale biases for Methods used, ES type, and Driver group.
## Warning: Factor `clean_answer` contains implicit NA, consider using
## `forcats::fct_explicit_na`
In this section, the dashed line represents what we would expect if subgroups were selected at random from the overall distribution from Yes’s and No’s. Thus, bars larger or smaller than the dashed line show that either time or space is more/less likely to be considered in that group. (I’m still working on how to write this up more specifically in a non-confusing way…so let me know if you have questions!). It’s also useful to keep in mind that these proportions can also be considered in absolute terms, not just with respect to the dashed line. So, for example, even if something is more likely than expected to be multi-scale, it still may be the case that less than half of the studies in that group are multi-scale (which would still point to some sort of absolute gap).
For temporal trends, a study was considered as a ‘Yes’ if it used ‘space for time’ to simplify plotting.
In the plot below, the dashed line represents the overall proportion of multi-scale studies given that the study considered temporal trends (since that was the only way to get the number of years).
## Warning: Factor `YrsData` contains implicit NA, consider using
## `forcats::fct_explicit_na`
For these biases plots, a single paper can fit into multiple groups (e.g. a paper that used Observational and Model/data simulation methods). The dashed line accounts for this, so that it still serves as a ‘random expectation’, but it is a little complicated to explain (I’m working on putting together a little explanation with an example though!).
Unlike the above plots which double count papers that are in multiple groups, these plots keep papers single counted because it shows the counts and not proportions. These also keep all intersecting groups separate instead of only looking at the groups individually. These plots are a little messier, but I’m working on a way to better show these patterns (if it seems worth it).
## Warning: Factor `YrsData` contains implicit NA, consider using
## `forcats::fct_explicit_na`
Unlike the above plots which double count papers that are in multiple groups, these plots keep papers single counted because it shows the counts and not proportions. These also keep all intersecting groups separate instead of only looking at the groups individually. These plots are a little messier, but I’m working on a way to better show these patterns (if it seems worth it).
For these biases plots, a single paper can fit into multiple groups (e.g. a paper that used Observational and Model/data simulation methods). The dashed line accounts for this, so that it still serves as a ‘random expectation’, but it is a little complicated to explain (I’m working on putting together a little explanation with an example though!).
Unlike the above plots which double count papers that are in multiple groups, these plots keep papers single counted because it shows the counts and not proportions. These also keep all intersecting groups separate instead of only looking at the groups individually. These plots are a little messier, but I’m working on a way to better show these patterns (if it seems worth it).
## Warning: Factor `YrsData` contains implicit NA, consider using
## `forcats::fct_explicit_na`
Unlike the above plots which double count papers that are in multiple groups, these plots keep papers single counted because it shows the counts and not proportions. These also keep all intersecting groups separate instead of only looking at the groups individually. These plots are a little messier, but I’m working on a way to better show these patterns (if it seems worth it).
For these biases plots, a single paper can fit into multiple groups (e.g. a paper that used Observational and Model/data simulation methods). The dashed line accounts for this, so that it still serves as a ‘random expectation’, but it is a little complicated to explain (I’m working on putting together a little explanation with an example though!).
Unlike the above plots which double count papers that are in multiple groups, these plots keep papers single counted because it shows the counts and not proportions. These also keep all intersecting groups separate instead of only looking at the groups individually. These plots are a little messier, but I’m working on a way to better show these patterns (if it seems worth it).
## Warning: Factor `YrsData` contains implicit NA, consider using
## `forcats::fct_explicit_na`
Unlike the above plots which double count papers that are in multiple groups, these plots keep papers single counted because it shows the counts and not proportions. These also keep all intersecting groups separate instead of only looking at the groups individually. These plots are a little messier, but I’m working on a way to better show these patterns (if it seems worth it).
## # A tibble: 8 x 2
## clean_answer count
## <fct> <int>
## 1 Ecosystem function -> service providers 13
## 2 Ecosystem function -> service providers,No feedbacks measured directly 1
## 3 Ecosystem function / service -> abiotic drivers 2
## 4 Ecosystem function / service -> abiotic drivers,Ecosystem function -> s… 1
## 5 Ecosystem services -> ecosystem function 3
## 6 Ecosystem services -> service providers 2
## 7 No feedbacks measured directly 249
## 8 Service providers -> abiotic drivers,Ecosystem services -> service prov… 1